diff --git a/docs/requirements.txt b/docs/requirements.txt
index 007281ac35..6caddce666 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -40,3 +40,4 @@ onnx>=1.13.0
 onnxruntime; python_version <= '3.10'
 zarr
 huggingface_hub
+pyamg>=5.0.0
diff --git a/docs/source/installation.md b/docs/source/installation.md
index d77253f0f9..644dd623c1 100644
--- a/docs/source/installation.md
+++ b/docs/source/installation.md
@@ -258,6 +258,6 @@ Since MONAI v0.2.0, the extras syntax such as `pip install 'monai[nibabel]'` is
 ```
 
 which correspond to `nibabel`, `scikit-image`,`scipy`, `pillow`, `tensorboard`,
-`gdown`, `pytorch-ignite`, `torchvision`, `itk`, `tqdm`, `lmdb`, `psutil`, `cucim`, `openslide-python`, `pandas`, `einops`, `transformers`, `mlflow`, `clearml`, `matplotlib`, `tensorboardX`, `tifffile`, `imagecodecs`, `pyyaml`, `fire`, `jsonschema`, `ninja`, `pynrrd`, `pydicom`, `h5py`, `nni`, `optuna`, `onnx`, `onnxruntime`, `zarr`, `lpips`, `nvidia-ml-py`, and `huggingface_hub` respectively.
+`gdown`, `pytorch-ignite`, `torchvision`, `itk`, `tqdm`, `lmdb`, `psutil`, `cucim`, `openslide-python`, `pandas`, `einops`, `transformers`, `mlflow`, `clearml`, `matplotlib`, `tensorboardX`, `tifffile`, `imagecodecs`, `pyyaml`, `fire`, `jsonschema`, `ninja`, `pynrrd`, `pydicom`, `h5py`, `nni`, `optuna`, `onnx`, `onnxruntime`, `zarr`, `lpips`, `nvidia-ml-py`, `huggingface_hub` and `pyamg` respectively.
 
 - `pip install 'monai[all]'` installs all the optional dependencies.
diff --git a/monai/data/ultrasound_confidence_map.py b/monai/data/ultrasound_confidence_map.py
index 03813e7559..5c716b62be 100644
--- a/monai/data/ultrasound_confidence_map.py
+++ b/monai/data/ultrasound_confidence_map.py
@@ -21,7 +21,9 @@
 cv2, _ = optional_import("cv2")
 csc_matrix, _ = optional_import("scipy.sparse", "1.7.1", min_version, "csc_matrix")
 spsolve, _ = optional_import("scipy.sparse.linalg", "1.7.1", min_version, "spsolve")
+cg, _ = optional_import("scipy.sparse.linalg", "1.7.1", min_version, "cg")
 hilbert, _ = optional_import("scipy.signal", "1.7.1", min_version, "hilbert")
+ruge_stuben_solver, _ = optional_import("pyamg", "5.0.0", min_version, "ruge_stuben_solver")
 
 
 class UltrasoundConfidenceMap:
@@ -30,6 +32,9 @@ class UltrasoundConfidenceMap:
     It generates a confidence map by setting source and sink points in the image and computing the probability
     for random walks to reach the source for each pixel.
 
+    The official code is available at:
+    https://campar.in.tum.de/Main/AthanasiosKaramalisCode
+
     Args:
         alpha (float, optional): Alpha parameter. Defaults to 2.0.
         beta (float, optional): Beta parameter. Defaults to 90.0.
@@ -37,15 +42,33 @@ class UltrasoundConfidenceMap:
         mode (str, optional): 'RF' or 'B' mode data. Defaults to 'B'.
         sink_mode (str, optional): Sink mode. Defaults to 'all'. If 'mask' is selected, a mask must be when calling
             the transform. Can be 'all', 'mid', 'min', or 'mask'.
+        use_cg (bool, optional): Use Conjugate Gradient method for solving the linear system. Defaults to False.
+        cg_tol (float, optional): Tolerance for the Conjugate Gradient method. Defaults to 1e-6.
+            Will be used only if `use_cg` is True.
+        cg_maxiter (int, optional): Maximum number of iterations for the Conjugate Gradient method. Defaults to 200.
+            Will be used only if `use_cg` is True.
     """
 
-    def __init__(self, alpha: float = 2.0, beta: float = 90.0, gamma: float = 0.05, mode="B", sink_mode="all"):
+    def __init__(
+        self,
+        alpha: float = 2.0,
+        beta: float = 90.0,
+        gamma: float = 0.05,
+        mode="B",
+        sink_mode="all",
+        use_cg=False,
+        cg_tol=1e-6,
+        cg_maxiter=200,
+    ):
         # The hyperparameters for confidence map estimation
         self.alpha = alpha
         self.beta = beta
         self.gamma = gamma
         self.mode = mode
         self.sink_mode = sink_mode
+        self.use_cg = use_cg
+        self.cg_tol = cg_tol
+        self.cg_maxiter = cg_maxiter
 
         # The precision to use for all computations
         self.eps = np.finfo("float64").eps
@@ -228,17 +251,18 @@ def confidence_laplacian(self, padded_index: NDArray, padded_image: NDArray, bet
         s = self.normalize(s)
 
         # Horizontal penalty
-        s[:vertical_end] += gamma
-        # s[vertical_end:diagonal_end] += gamma * np.sqrt(2) # --> In the paper it is sqrt(2)
-        # since the diagonal edges are longer yet does not exist in the original code
+        s[vertical_end:] += gamma
+        # Here there is a difference between the official MATLAB code and the paper
+        # on the edge penalty. We directly implement what the official code does.
 
         # Normalize differences
         s = self.normalize(s)
 
         # Gaussian weighting function
         s = -(
-            (np.exp(-beta * s, dtype="float64")) + 1.0e-6
-        )  # --> This epsilon changes results drastically default: 1.e-6
+            (np.exp(-beta * s, dtype="float64")) + 1e-5
+        )  # --> This epsilon changes results drastically default: 10e-6
+        # Please notice that it is not 1e-6, it is 10e-6 which is actually different.
 
         # Create Laplacian, diagonal missing
         lap = csc_matrix((s, (i, j)))
@@ -256,7 +280,14 @@ def confidence_laplacian(self, padded_index: NDArray, padded_image: NDArray, bet
         return lap
 
     def _solve_linear_system(self, lap, rhs):
-        x = spsolve(lap, rhs)
+
+        if self.use_cg:
+            lap_sparse = lap.tocsr()
+            ml = ruge_stuben_solver(lap_sparse, coarse_solver="pinv")
+            m = ml.aspreconditioner(cycle="V")
+            x, _ = cg(lap, rhs, tol=self.cg_tol, maxiter=self.cg_maxiter, M=m)
+        else:
+            x = spsolve(lap, rhs)
 
         return x
 
diff --git a/monai/transforms/intensity/array.py b/monai/transforms/intensity/array.py
index f656475a36..3b813809e4 100644
--- a/monai/transforms/intensity/array.py
+++ b/monai/transforms/intensity/array.py
@@ -2789,6 +2789,9 @@ class UltrasoundConfidenceMapTransform(Transform):
     It generates a confidence map by setting source and sink points in the image and computing the probability
     for random walks to reach the source for each pixel.
 
+    The official code is available at:
+    https://campar.in.tum.de/Main/AthanasiosKaramalisCode
+
     Args:
         alpha (float, optional): Alpha parameter. Defaults to 2.0.
         beta (float, optional): Beta parameter. Defaults to 90.0.
@@ -2796,14 +2799,32 @@ class UltrasoundConfidenceMapTransform(Transform):
         mode (str, optional): 'RF' or 'B' mode data. Defaults to 'B'.
         sink_mode (str, optional): Sink mode. Defaults to 'all'. If 'mask' is selected, a mask must be when
             calling the transform. Can be one of 'all', 'mid', 'min', 'mask'.
+        use_cg (bool, optional): Use Conjugate Gradient method for solving the linear system. Defaults to False.
+        cg_tol (float, optional): Tolerance for the Conjugate Gradient method. Defaults to 1e-6.
+            Will be used only if `use_cg` is True.
+        cg_maxiter (int, optional): Maximum number of iterations for the Conjugate Gradient method. Defaults to 200.
+            Will be used only if `use_cg` is True.
     """
 
-    def __init__(self, alpha: float = 2.0, beta: float = 90.0, gamma: float = 0.05, mode="B", sink_mode="all") -> None:
+    def __init__(
+        self,
+        alpha: float = 2.0,
+        beta: float = 90.0,
+        gamma: float = 0.05,
+        mode="B",
+        sink_mode="all",
+        use_cg=False,
+        cg_tol: float = 1.0e-6,
+        cg_maxiter: int = 200,
+    ):
         self.alpha = alpha
         self.beta = beta
         self.gamma = gamma
         self.mode = mode
         self.sink_mode = sink_mode
+        self.use_cg = use_cg
+        self.cg_tol = cg_tol
+        self.cg_maxiter = cg_maxiter
 
         if self.mode not in ["B", "RF"]:
             raise ValueError(f"Unknown mode: {self.mode}. Supported modes are 'B' and 'RF'.")
@@ -2813,7 +2834,9 @@ def __init__(self, alpha: float = 2.0, beta: float = 90.0, gamma: float = 0.05,
                 f"Unknown sink mode: {self.sink_mode}. Supported modes are 'all', 'mid', 'min' and 'mask'."
             )
 
-        self._compute_conf_map = UltrasoundConfidenceMap(self.alpha, self.beta, self.gamma, self.mode, self.sink_mode)
+        self._compute_conf_map = UltrasoundConfidenceMap(
+            self.alpha, self.beta, self.gamma, self.mode, self.sink_mode, self.use_cg, self.cg_tol, self.cg_maxiter
+        )
 
     def __call__(self, img: NdarrayOrTensor, mask: NdarrayOrTensor | None = None) -> NdarrayOrTensor:
         """Compute confidence map from an ultrasound image.
diff --git a/requirements-dev.txt b/requirements-dev.txt
index a8ba25966b..517c842d1e 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -57,3 +57,4 @@ zarr
 lpips==0.1.4
 nvidia-ml-py
 huggingface_hub
+pyamg>=5.0.0
diff --git a/setup.cfg b/setup.cfg
index 7b82784a8a..05bf181c70 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -84,6 +84,7 @@ all =
     lpips==0.1.4
     nvidia-ml-py
     huggingface_hub
+    pyamg>=5.0.0
 nibabel =
     nibabel
 ninja =
@@ -162,6 +163,8 @@ pynvml =
 #     MetricsReloaded @ git+https://github.com/Project-MONAI/MetricsReloaded@monai-support#egg=MetricsReloaded
 huggingface_hub =
     huggingface_hub
+pyamg =
+    pyamg>=5.0.0
 
 [flake8]
 select = B,C,E,F,N,P,T4,W,B9
diff --git a/tests/test_ultrasound_confidence_map_transform.py b/tests/test_ultrasound_confidence_map_transform.py
index 63ce7d58e4..87c08b3ac3 100644
--- a/tests/test_ultrasound_confidence_map_transform.py
+++ b/tests/test_ultrasound_confidence_map_transform.py
@@ -11,11 +11,13 @@
 
 from __future__ import annotations
 
+import os
 import unittest
 
 import numpy as np
 import torch
 from parameterized import parameterized
+from PIL import Image
 
 from monai.transforms import UltrasoundConfidenceMapTransform
 from tests.utils import assert_allclose
@@ -32,7 +34,8 @@
         [1, 2, 3, 32, 33, 34, 35, 1, 2, 3],
         [1, 2, 3, 36, 37, 38, 39, 1, 2, 3],
         [1, 2, 3, 40, 41, 42, 43, 1, 2, 3],
-    ]
+    ],
+    dtype=np.float32,
 )
 
 TEST_MASK = np.array(
@@ -47,474 +50,435 @@
         [1, 1, 1, 0, 0, 0, 1, 1, 1, 0],
         [1, 1, 1, 0, 0, 0, 1, 1, 1, 0],
         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-    ]
+    ],
+    dtype=np.float32,
 )
 
 SINK_ALL_OUTPUT = np.array(
     [
         [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
         [
-            0.97514489,
-            0.96762971,
-            0.96164186,
-            0.95463443,
-            0.9941512,
-            0.99023054,
-            0.98559401,
-            0.98230057,
-            0.96601224,
-            0.95119599,
-        ],
-        [
-            0.92960533,
-            0.92638451,
-            0.9056675,
-            0.9487176,
-            0.9546961,
-            0.96165853,
-            0.96172303,
-            0.92686401,
-            0.92122613,
-            0.89957239,
-        ],
-        [
-            0.86490963,
-            0.85723665,
-            0.83798141,
-            0.90816201,
-            0.90816097,
-            0.90815301,
-            0.9081427,
-            0.85933627,
-            0.85146935,
-            0.82948586,
-        ],
-        [
-            0.77430346,
-            0.76731372,
-            0.74372311,
-            0.89128774,
-            0.89126885,
-            0.89125066,
-            0.89123521,
-            0.76858589,
-            0.76106647,
-            0.73807776,
-        ],
-        [
-            0.66098109,
-            0.65327697,
-            0.63090644,
-            0.33086588,
-            0.3308383,
-            0.33081937,
-            0.33080718,
-            0.6557468,
-            0.64825099,
-            0.62593375,
-        ],
-        [
-            0.52526945,
-            0.51832586,
-            0.49709412,
-            0.25985059,
-            0.25981009,
-            0.25977729,
-            0.25975222,
-            0.52118958,
-            0.51426328,
-            0.49323164,
-        ],
-        [
-            0.3697845,
-            0.36318971,
-            0.34424661,
-            0.17386804,
-            0.17382046,
-            0.17377993,
-            0.17374668,
-            0.36689317,
-            0.36036096,
-            0.3415582,
-        ],
-        [
-            0.19546374,
-            0.1909659,
-            0.17319999,
-            0.08423318,
-            0.08417993,
-            0.08413242,
-            0.08409104,
-            0.19393909,
-            0.18947485,
-            0.17185031,
+            0.8884930952884654,
+            0.8626656901726876,
+            0.8301161870669913,
+            0.9757179300830185,
+            0.9989819637626414,
+            0.9994717624885747,
+            0.9954377526794013,
+            0.8898638133944221,
+            0.862604343021387,
+            0.8277862494812598,
+        ],
+        [
+            0.7765718877433174,
+            0.7363731552518268,
+            0.6871875923653379,
+            0.9753673327387775,
+            0.9893175316399789,
+            0.9944181334242039,
+            0.9936979128319371,
+            0.7778001700035326,
+            0.7362622619974832,
+            0.6848377775329241,
+        ],
+        [
+            0.6648416226360719,
+            0.6178079903692397,
+            0.5630152545966568,
+            0.8278402502498404,
+            0.82790391019578,
+            0.8289702087149963,
+            0.8286730258710652,
+            0.6658773633169731,
+            0.6176836507071695,
+            0.5609165245633834,
+        ],
+        [
+            0.5534420483956817,
+            0.5055401989946189,
+            0.451865872383879,
+            0.7541423053657541,
+            0.7544115886347456,
+            0.7536884376055174,
+            0.7524927915364896,
+            0.5542943466824017,
+            0.505422678400297,
+            0.4502051549732117,
+        ],
+        [
+            0.4423657561928356,
+            0.398221575954319,
+            0.35030055029978124,
+            0.4793202144786371,
+            0.48057175662074125,
+            0.4812057229564038,
+            0.48111949176149327,
+            0.44304092606050766,
+            0.39812149713417405,
+            0.34902458531143377,
+        ],
+        [
+            0.3315561576450342,
+            0.29476346732036784,
+            0.2558303772864961,
+            0.35090405668257535,
+            0.3515225984307705,
+            0.35176548159366317,
+            0.3516979775419521,
+            0.33205839061494885,
+            0.2946859567272435,
+            0.2549042599220772,
+        ],
+        [
+            0.22094175240967673,
+            0.19431840633358133,
+            0.16672448058324435,
+            0.22716195845848167,
+            0.22761996456848282,
+            0.22782525614780919,
+            0.22781876632199002,
+            0.22127471252104777,
+            0.19426593309729956,
+            0.16612306610996525,
+        ],
+        [
+            0.11044782531624744,
+            0.09623229814933323,
+            0.08174664901235043,
+            0.11081911718888311,
+            0.11102310514207447,
+            0.1111041051969924,
+            0.11108329076967229,
+            0.11061376973431204,
+            0.09620592927336903,
+            0.08145227209865454,
         ],
         [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
-    ]
+    ],
+    dtype=np.float32,
 )
 
 SINK_MID_OUTPUT = np.array(
     [
+        [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
         [
-            1.00000000e00,
-            1.00000000e00,
-            1.00000000e00,
-            1.00000000e00,
-            1.00000000e00,
-            1.00000000e00,
-            1.00000000e00,
-            1.00000000e00,
-            1.00000000e00,
-            1.00000000e00,
-        ],
-        [
-            9.99996103e-01,
-            9.99994823e-01,
-            9.99993550e-01,
-            9.99930863e-01,
-            9.99990782e-01,
-            9.99984683e-01,
-            9.99979000e-01,
-            9.99997804e-01,
-            9.99995985e-01,
-            9.99994325e-01,
-        ],
-        [
-            9.99989344e-01,
-            9.99988600e-01,
-            9.99984099e-01,
-            9.99930123e-01,
-            9.99926598e-01,
-            9.99824297e-01,
-            9.99815032e-01,
-            9.99991228e-01,
-            9.99990881e-01,
-            9.99988462e-01,
-        ],
-        [
-            9.99980787e-01,
-            9.99979264e-01,
-            9.99975828e-01,
-            9.59669286e-01,
-            9.59664779e-01,
-            9.59656566e-01,
-            9.59648332e-01,
-            9.99983882e-01,
-            9.99983038e-01,
-            9.99980732e-01,
-        ],
-        [
-            9.99970181e-01,
-            9.99969032e-01,
-            9.99965730e-01,
-            9.45197806e-01,
-            9.45179593e-01,
-            9.45163629e-01,
-            9.45151458e-01,
-            9.99973352e-01,
-            9.99973254e-01,
-            9.99971098e-01,
-        ],
-        [
-            9.99958608e-01,
-            9.99957307e-01,
-            9.99953444e-01,
-            4.24743523e-01,
-            4.24713305e-01,
-            4.24694646e-01,
-            4.24685271e-01,
-            9.99960948e-01,
-            9.99961829e-01,
-            9.99960347e-01,
-        ],
-        [
-            9.99946675e-01,
-            9.99945139e-01,
-            9.99940312e-01,
-            3.51353224e-01,
-            3.51304003e-01,
-            3.51268260e-01,
-            3.51245366e-01,
-            9.99947688e-01,
-            9.99950165e-01,
-            9.99949512e-01,
-        ],
-        [
-            9.99935877e-01,
-            9.99934088e-01,
-            9.99928982e-01,
-            2.51197134e-01,
-            2.51130273e-01,
-            2.51080014e-01,
-            2.51045852e-01,
-            9.99936187e-01,
-            9.99939716e-01,
-            9.99940022e-01,
-        ],
-        [
-            9.99927846e-01,
-            9.99925911e-01,
-            9.99920188e-01,
-            1.31550973e-01,
-            1.31462736e-01,
-            1.31394558e-01,
-            1.31346069e-01,
-            9.99927275e-01,
-            9.99932142e-01,
-            9.99933313e-01,
-        ],
-        [
-            9.99924204e-01,
-            9.99922004e-01,
-            9.99915767e-01,
-            3.04861147e-04,
-            1.95998056e-04,
-            0.00000000e00,
-            2.05182682e-05,
-            9.99923115e-01,
-            9.99928835e-01,
-            9.99930535e-01,
-        ],
-    ]
+            0.9999957448889315,
+            0.9999781044114231,
+            0.9999142422442185,
+            0.999853253199584,
+            0.9999918403054282,
+            0.9999874855193227,
+            0.9999513619364747,
+            0.9999589247003497,
+            0.9999861765528631,
+            0.9999939213967494,
+        ],
+        [
+            0.9999918011366045,
+            0.9999588498417253,
+            0.9998388659316617,
+            0.9998496524281603,
+            0.9999154673258592,
+            0.9997827845182361,
+            0.9998160234579786,
+            0.9999163964511287,
+            0.9999743435786168,
+            0.9999894752861168,
+        ],
+        [
+            0.9999883847481621,
+            0.9999427334014465,
+            0.9997703972600652,
+            0.9853967608835997,
+            0.9852517829915376,
+            0.9853308520519438,
+            0.9854102394414211,
+            0.9998728503298413,
+            0.9999642585978225,
+            0.999986204909933,
+        ],
+        [
+            0.999985544721449,
+            0.9999296195017368,
+            0.9997066149628903,
+            0.9753803016111353,
+            0.9750688049429371,
+            0.9749211929217173,
+            0.9750052047129354,
+            0.9998284130289159,
+            0.9999558481338295,
+            0.9999837966320273,
+        ],
+        [
+            0.9999832723447848,
+            0.9999192263814408,
+            0.9996472692076177,
+            0.90541293509353,
+            0.9049945536526819,
+            0.9051142437853055,
+            0.9057005861296792,
+            0.9997839348839027,
+            0.9999490318922627,
+            0.9999820419085812,
+        ],
+        [
+            0.9999815409510937,
+            0.9999113168889934,
+            0.9995930143319085,
+            0.8370025145062345,
+            0.8358345435164332,
+            0.8358231468627223,
+            0.8369430449157075,
+            0.9997408260265034,
+            0.9999437526409107,
+            0.9999808010740554,
+        ],
+        [
+            0.9999803198262347,
+            0.9999057164296593,
+            0.9995461103528891,
+            0.7047260555380003,
+            0.7023346743490383,
+            0.7022946969603594,
+            0.7045662738042475,
+            0.9997017258131392,
+            0.9999399744001316,
+            0.9999799785302944,
+        ],
+        [
+            0.9999795785255197,
+            0.9999022923125928,
+            0.999510772973329,
+            0.46283993237260707,
+            0.4577365087549323,
+            0.4571888733219068,
+            0.4614967878524538,
+            0.9996710272733927,
+            0.9999376682163403,
+            0.9999795067125865,
+        ],
+        [
+            0.9999792877553907,
+            0.9999009179811408,
+            0.9994950057121632,
+            0.05049460567213739,
+            0.030946131978013824,
+            0.0,
+            0.019224121648385283,
+            0.9996568912408903,
+            0.9999367861122628,
+            0.9999793358521326,
+        ],
+    ],
+    dtype=np.float32,
 )
 
 SINK_MIN_OUTPUT = np.array(
     [
         [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
         [
-            0.99997545,
-            0.99996582,
-            0.99995245,
-            0.99856594,
-            0.99898314,
-            0.99777223,
-            0.99394423,
-            0.98588113,
-            0.97283215,
-            0.96096504,
-        ],
-        [
-            0.99993872,
-            0.99993034,
-            0.9998832,
-            0.9986147,
-            0.99848741,
-            0.9972981,
-            0.99723719,
-            0.94157173,
-            0.9369832,
-            0.91964243,
-        ],
-        [
-            0.99990802,
-            0.99989475,
-            0.99986873,
-            0.98610197,
-            0.98610047,
-            0.98609749,
-            0.98609423,
-            0.88741275,
-            0.88112911,
-            0.86349156,
-        ],
-        [
-            0.99988924,
-            0.99988509,
-            0.99988698,
-            0.98234089,
-            0.98233591,
-            0.98233065,
-            0.98232562,
-            0.81475172,
-            0.80865978,
-            0.79033138,
-        ],
-        [
-            0.99988418,
-            0.99988484,
-            0.99988323,
-            0.86796555,
-            0.86795874,
-            0.86795283,
-            0.86794756,
-            0.72418193,
-            0.71847704,
-            0.70022037,
-        ],
-        [
-            0.99988241,
-            0.99988184,
-            0.99988103,
-            0.85528225,
-            0.85527303,
-            0.85526389,
-            0.85525499,
-            0.61716519,
-            0.61026209,
-            0.59503671,
-        ],
-        [
-            0.99988015,
-            0.99987985,
-            0.99987875,
-            0.84258114,
-            0.84257121,
-            0.84256042,
-            0.84254897,
-            0.48997924,
-            0.49083978,
-            0.46891561,
-        ],
-        [
-            0.99987865,
-            0.99987827,
-            0.9998772,
-            0.83279589,
-            0.83278624,
-            0.83277384,
-            0.83275897,
-            0.36345545,
-            0.33690244,
-            0.35696828,
-        ],
-        [
-            0.99987796,
-            0.99987756,
-            0.99987643,
-            0.82873223,
-            0.82872648,
-            0.82871803,
-            0.82870711,
-            0.0,
-            0.26106012,
-            0.29978657,
-        ],
-    ]
+            0.9999961997987318,
+            0.9999801752476248,
+            0.9999185667341594,
+            0.9993115972922259,
+            0.9999536433504382,
+            0.9997590064584757,
+            0.9963282396026231,
+            0.9020645423682648,
+            0.965641014946897,
+            0.9847003633599846,
+        ],
+        [
+            0.9999926824858815,
+            0.9999628275604145,
+            0.9998472915971415,
+            0.9992953054409239,
+            0.9995550237000549,
+            0.9972853256638443,
+            0.9958871482234863,
+            0.8006505271617617,
+            0.9360757301263053,
+            0.9734843475613124,
+        ],
+        [
+            0.9999896427490426,
+            0.9999484707116104,
+            0.9997841142091455,
+            0.9321779021295554,
+            0.9308591506422442,
+            0.9299937642438358,
+            0.9286536283468563,
+            0.6964658886602826,
+            0.9106656689679997,
+            0.9652109119709528,
+        ],
+        [
+            0.9999871227708508,
+            0.9999369646510842,
+            0.9997276125796202,
+            0.9006206490361908,
+            0.8987968702587018,
+            0.8965696900664386,
+            0.8941507574801211,
+            0.5892568658180841,
+            0.8892240419729905,
+            0.9590996257620853,
+        ],
+        [
+            0.9999851119906539,
+            0.9999280075234918,
+            0.9996788394671484,
+            0.778755271203017,
+            0.7763917808258874,
+            0.7737517385551721,
+            0.7707980517990098,
+            0.4788014936236403,
+            0.8715671104783401,
+            0.954632732759503,
+        ],
+        [
+            0.9999835837292402,
+            0.999921323618806,
+            0.9996389455307461,
+            0.7222961578407286,
+            0.7186158832946955,
+            0.7146983167265393,
+            0.7105768254632475,
+            0.3648911004360315,
+            0.8575943501305144,
+            0.9514642802768379,
+        ],
+        [
+            0.9999825081019064,
+            0.999916683268467,
+            0.9996093996776352,
+            0.6713490686473397,
+            0.6664914636518112,
+            0.6613110504728309,
+            0.6558325489984669,
+            0.247299682539502,
+            0.8473037957967624,
+            0.9493580587294981,
+        ],
+        [
+            0.999981856118739,
+            0.9999138938063622,
+            0.9995907248497593,
+            0.6331535096751639,
+            0.6271637176135582,
+            0.6206687804556549,
+            0.6136262027168252,
+            0.12576864809108962,
+            0.8407892431959736,
+            0.9481472656653798,
+        ],
+        [
+            0.9999816006081851,
+            0.9999127861527936,
+            0.9995832399159849,
+            0.6133274396648696,
+            0.6086364734302403,
+            0.6034602717119345,
+            0.5978473214165134,
+            0.0,
+            0.8382338778894218,
+            0.9477082231321966,
+        ],
+    ],
+    dtype=np.float32,
 )
 
 SINK_MASK_OUTPUT = np.array(
     [
+        [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
+        [0.0, 0.0, 0.0, 0.9047934405899283, 0.9936046284605553, 0.9448690902377527, 0.0, 0.0, 0.0, 0.8363773255131761],
+        [0.0, 0.0, 0.0, 0.90375200446097, 0.9434594475474036, 0.4716831449516178, 0.0, 0.0, 0.0, 0.7364197333910302],
+        [
+            0.0,
+            0.0,
+            0.0,
+            0.09080438801405301,
+            0.06774182873204163,
+            0.038207095016625024,
+            0.0,
+            0.0,
+            0.0,
+            0.6745641479264269,
+        ],
+        [
+            0.0,
+            0.0,
+            0.0,
+            0.01731082802870267,
+            0.013540929458217351,
+            0.007321202161532623,
+            0.0,
+            0.0,
+            0.0,
+            0.6341231654271253,
+        ],
+        [
+            0.0,
+            0.0,
+            0.0,
+            0.0006444251665178544,
+            0.0005397129128756325,
+            0.0003048384803626333,
+            0.0,
+            0.0,
+            0.0,
+            0.6070178708536365,
+        ],
+        [
+            0.0,
+            0.0,
+            0.0,
+            5.406078586212675e-05,
+            4.416783924970537e-05,
+            2.4597362039020103e-05,
+            0.0,
+            0.0,
+            0.0,
+            0.5889413683184284,
+        ],
         [
-            1.00000000e00,
-            1.00000000e00,
-            1.00000000e00,
-            1.00000000e00,
-            1.00000000e00,
-            1.00000000e00,
-            1.00000000e00,
-            1.00000000e00,
-            1.00000000e00,
-            1.00000000e00,
-        ],
-        [
-            0.00000000e00,
-            0.00000000e00,
-            0.00000000e00,
-            2.86416400e-01,
-            7.93271181e-01,
-            5.81341234e-01,
-            0.00000000e00,
-            0.00000000e00,
-            0.00000000e00,
-            1.98395623e-01,
-        ],
-        [
-            0.00000000e00,
-            0.00000000e00,
-            0.00000000e00,
-            2.66733297e-01,
-            2.80741490e-01,
-            4.14078784e-02,
-            0.00000000e00,
-            0.00000000e00,
-            0.00000000e00,
-            7.91676486e-04,
-        ],
-        [
-            0.00000000e00,
-            0.00000000e00,
-            0.00000000e00,
-            1.86244537e-04,
-            1.53413401e-04,
-            7.85806495e-05,
-            0.00000000e00,
-            0.00000000e00,
-            0.00000000e00,
-            5.09797387e-06,
-        ],
-        [
-            0.00000000e00,
-            0.00000000e00,
-            0.00000000e00,
-            9.62904581e-07,
-            7.23946225e-07,
-            3.68824440e-07,
-            0.00000000e00,
-            0.00000000e00,
-            0.00000000e00,
-            4.79525316e-08,
-        ],
-        [
-            0.00000000e00,
-            0.00000000e00,
-            0.00000000e00,
-            1.50939343e-10,
-            1.17724874e-10,
-            6.21760843e-11,
-            0.00000000e00,
-            0.00000000e00,
-            0.00000000e00,
-            6.08922784e-10,
-        ],
-        [
-            0.00000000e00,
-            0.00000000e00,
-            0.00000000e00,
-            2.57593754e-13,
-            1.94066716e-13,
-            9.83784370e-14,
-            0.00000000e00,
-            0.00000000e00,
-            0.00000000e00,
-            9.80828665e-12,
-        ],
-        [
-            0.00000000e00,
-            0.00000000e00,
-            0.00000000e00,
-            4.22323494e-16,
-            3.17556633e-16,
-            1.60789400e-16,
-            0.00000000e00,
-            0.00000000e00,
-            0.00000000e00,
-            1.90789819e-13,
-        ],
-        [
-            0.00000000e00,
-            0.00000000e00,
-            0.00000000e00,
-            7.72677888e-19,
-            5.83029424e-19,
-            2.95946659e-19,
-            0.00000000e00,
-            0.00000000e00,
-            0.00000000e00,
-            4.97038275e-15,
-        ],
-        [
-            2.71345908e-24,
-            5.92006757e-24,
-            2.25580089e-23,
-            3.82601970e-18,
-            3.82835349e-18,
-            3.83302158e-18,
-            3.84002606e-18,
-            8.40760586e-16,
-            1.83433696e-15,
-            1.11629633e-15,
-        ],
-    ]
+            0.0,
+            0.0,
+            0.0,
+            4.39259327223233e-06,
+            3.6050656774754658e-06,
+            2.0127120155893425e-06,
+            0.0,
+            0.0,
+            0.0,
+            0.5774279920364456,
+        ],
+        [
+            0.0,
+            0.0,
+            0.0,
+            4.0740501726718113e-07,
+            3.374875487404489e-07,
+            1.9113630985667455e-07,
+            0.0,
+            0.0,
+            0.0,
+            0.5709897726747111,
+        ],
+        [
+            3.2266922388030425e-17,
+            1.801110982679718e-14,
+            9.325899448306927e-12,
+            3.913608442133728e-07,
+            3.9581822403393465e-07,
+            4.02383505118481e-07,
+            4.14820241328287e-07,
+            4.281640797396309e-06,
+            0.0023900192231620593,
+            0.5686882523793125,
+        ],
+    ],
+    dtype=np.float32,
 )
 
 
@@ -527,6 +491,21 @@ def setUp(self):
         self.input_img_torch = torch.from_numpy(TEST_INPUT).unsqueeze(0)  # mock image (torch tensor)
         self.input_mask_torch = torch.from_numpy(TEST_MASK).unsqueeze(0)  # mock mask (torch tensor)
 
+        self.real_input_img_paths = [
+            os.path.join(os.path.dirname(__file__), "testing_data", "ultrasound_confidence_map", "neck_input.png"),
+            os.path.join(os.path.dirname(__file__), "testing_data", "ultrasound_confidence_map", "femur_input.png"),
+        ]
+
+        self.real_result_npy_paths = [
+            os.path.join(os.path.dirname(__file__), "testing_data", "ultrasound_confidence_map", "neck_result.npy"),
+            os.path.join(os.path.dirname(__file__), "testing_data", "ultrasound_confidence_map", "femur_result.npy"),
+        ]
+
+        self.real_input_paramaters = [
+            {"alpha": 2.0, "beta": 90, "gamma": 0.03},
+            {"alpha": 2.0, "beta": 90, "gamma": 0.06},
+        ]
+
     def test_parameters(self):
         # Unknown mode
         with self.assertRaises(ValueError):
@@ -683,6 +662,44 @@ def test_func(self):
         output = transform(self.input_img_torch, self.input_mask_torch)
         assert_allclose(output, torch.tensor(SINK_MASK_OUTPUT), rtol=1e-4, atol=1e-4)
 
+    def test_against_official_code(self):
+        # This test is to compare the output of the transform with the official code
+        # The official code is available at:
+        # https://campar.in.tum.de/Main/AthanasiosKaramalisCode
+
+        for input_img_path, result_npy_path, params in zip(
+            self.real_input_img_paths, self.real_result_npy_paths, self.real_input_paramaters
+        ):
+            input_img = np.array(Image.open(input_img_path))
+            input_img = np.expand_dims(input_img, axis=0)
+
+            result_img = np.load(result_npy_path)
+
+            transform = UltrasoundConfidenceMapTransform(sink_mode="all", **params)
+            output = transform(input_img)
+
+            assert_allclose(output, result_img, rtol=1e-4, atol=1e-4)
+
+    def test_against_official_code_using_cg(self):
+        # This test is to compare the output of the transform with the official code
+        # The official code is available at:
+        # https://campar.in.tum.de/Main/AthanasiosKaramalisCode
+
+        for input_img_path, result_npy_path, params in zip(
+            self.real_input_img_paths, self.real_result_npy_paths, self.real_input_paramaters
+        ):
+            input_img = np.array(Image.open(input_img_path))
+            input_img = np.expand_dims(input_img, axis=0)
+
+            result_img = np.load(result_npy_path)
+
+            transform = UltrasoundConfidenceMapTransform(
+                sink_mode="all", use_cg=True, cg_tol=1.0e-6, cg_maxiter=300, **params
+            )
+            output = transform(input_img)
+
+            assert_allclose(output, result_img, rtol=1e-2, atol=1e-2)
+
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/tests/testing_data/ultrasound_confidence_map/femur_input.png b/tests/testing_data/ultrasound_confidence_map/femur_input.png
new file mode 100644
index 0000000000..0343e58720
Binary files /dev/null and b/tests/testing_data/ultrasound_confidence_map/femur_input.png differ
diff --git a/tests/testing_data/ultrasound_confidence_map/femur_result.npy b/tests/testing_data/ultrasound_confidence_map/femur_result.npy
new file mode 100644
index 0000000000..a3f322b113
Binary files /dev/null and b/tests/testing_data/ultrasound_confidence_map/femur_result.npy differ
diff --git a/tests/testing_data/ultrasound_confidence_map/neck_input.png b/tests/testing_data/ultrasound_confidence_map/neck_input.png
new file mode 100644
index 0000000000..74a64a9d90
Binary files /dev/null and b/tests/testing_data/ultrasound_confidence_map/neck_input.png differ
diff --git a/tests/testing_data/ultrasound_confidence_map/neck_result.npy b/tests/testing_data/ultrasound_confidence_map/neck_result.npy
new file mode 100644
index 0000000000..8bf760182c
Binary files /dev/null and b/tests/testing_data/ultrasound_confidence_map/neck_result.npy differ